Email this article VGG-RF Hybrid Model for Breast Cancer Classification in Medical Imaging
Abstract
Breast cancer remains a significant global health challenge, demanding rapid and accurate diagnostic solutions. This project presents a deep learning-based approach utilizing histopathological images to detect breast cancer. Various CNN architectures, including ResNet, AlexNet, GoogleNet, and VGG16, are tested. To address image variability and class imbalance, the CycleGAN model is applied for data augmentation and stain normalization. Notably, a hybrid model combining VGG16 feature extraction with Random Forest classification achieves 99% accuracy. This robust system supports pathologists by providing consistent, automated cancer detection, minimizing diagnostic errors and enhancing clinical decision-making. The model demonstrates strong potential for real-world deployment, combining deep learning's precision with the adaptability of image transformation techniques.
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References
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